A taxonomy of video lecture styles

Many educational organizations are employing instructional video in their pedagogy, but there is limited understanding of the possible presentation styles. In practice, the presentation style of video lectures ranges from a direct recording of classroom teaching with a stationary camera and screencasts with voice-over, up to highly elaborate video post-production. Previous work evaluated the effectiveness of several presentation styles, but there has not been any consistent taxonomy, which would have made comparisons and meta-analyses possible. In this article, we surveyed the research literature and we examined contemporary video-based courses, which have been produced by diverse educational organizations and teachers across various academic disciplines. We organized video lectures in two dimensions according to the level of human presence and according to the type of instructional media. In addition to organizing existing video lectures in a comprehensive way, the proposed taxonomy offers a design space that facilitates the choice of a suitable presentation style, as well as the preparation of new ones.Comment: 14 pages, 5 figure

User-based key frame detection in social web video

Video search results and suggested videos on web sites are represented with a video thumbnail, which is manually selected by the video up-loader among three randomly generated ones (e.g., YouTube). In contrast, we present a grounded user-based approach for automatically detecting interesting key-frames within a video through aggregated users' replay interactions with the video player. Previous research has focused on content-based systems that have the benefit of analyzing a video without user interactions, but they are monolithic, because the resulting video thumbnails are the same regardless of the user preferences. We constructed a user interest function, which is based on aggregate video replays, and analyzed hundreds of user interactions. We found that the local maximum of the replaying activity stands for the semantics of information rich videos, such as lecture, and how-to. The concept of user-based key-frame detection could be applied to any video on the web, in order to generate a user-based and dynamic video thumbnail in search results.Comment: 4 pages, 4 figure

Taking Social TV Beyond Chatting: How The TV Viewer Adds Value To The Network

In this paper, we provide a comprehensive overview of the state-of-the-art in a contemporary iTV research area: social and networked TV. In our approach, instead of considering research sub-topics that build upon particular disciplinary threads (e.g., usability, personalization, multimedia annotations), we take a multidisciplinary approach that builds upon findings in media studies, human-computer interaction and multimedia systems. Moreover, we downplay the importance of chatting over a distance in favor of non-verbal communication modalities. In contrast, we focus on Social TV practices and highlight the role of each viewer as a node that adds value to the TV network. Finally, we provide directions for further research in neglected topics, such as supporting collocated viewing, and sharing the TV experience in a seamless way

Making Sense of Video Analytics: Lessons Learned from Clickstream Interactions, Attitudes, and Learning Outcome in a Video-Assisted Course

Online video lectures have been considered an instructional media for various pedagogic approaches, such as the flipped classroom and open online courses. In comparison to other instructional media, online video affords the opportunity for recording student clickstream patterns within a video lecture. Video analytics within lecture videos may provide insights into student learning performance and inform the improvement of video-assisted teaching tactics. Nevertheless, video analytics are not accessible to learning stakeholders, such as researchers and educators, mainly because online video platforms do not broadly share the interactions of the users with their systems. For this purpose, we have designed an open-access video analytics system for use in a video-assisted course. In this paper, we present a longitudinal study, which provides valuable insights through the lens of the collected video analytics. In particular, we found that there is a relationship between video navigation (repeated views) and the level of cognition/thinking required for a specific video segment. Our results indicated that learning performance progress was slightly improved and stabilized after the third week of the video-assisted course. We also found that attitudes regarding easiness, usability, usefulness, and acceptance of this type of course remained at the same levels throughout the course. Finally, we triangulate analytics from diverse sources, discuss them, and provide the lessons learned for further development and refinement of video-assisted courses and practices

Video Pulses: User-Based Modeling of Interesting Video Segments

We present a user-based method that detects regions of interest within a video in order to provide video skims and video summaries. Previous research in video retrieval has focused on content-based techniques, such as pattern recognition algorithms that attempt to understand the low-level features of a video. We are proposing a pulse modeling method, which makes sense of a web video by analyzing users' Replay interactions with the video player. In particular, we have modeled the user information seeking behavior as a time series and the semantic regions as a discrete pulse of fixed width. Then, we have calculated the correlation coefficient between the dynamically detected pulses at the local maximums of the user activity signal and the pulse of reference. We have found that users' Replay activity significantly matches the important segments in information-rich and visually complex videos, such as lecture, how-to, and documentary. The proposed signal processing of user activity is complementary to previous work in content-based video retrieval and provides an additional user-based dimension for modeling the semantics of a social video on the web

The digital set-top box as a virtual channel provider

This research is based on the realization that the desktop computing paradigm is not appropriate for television, because it is adapted to fundamentally different user aspirations and activities. Instead, the virtual channel is a model that supports the organization and dynamic presentation of digital television programming from a combination of live broadcasts, prerecorded content and Internet resources at each set-top box. The goal is to design the respective framework of user interface patterns that consider the affective nature of television usability and facilitate the diversity of viewing situations